dSAIC Capabilities
dSAIC integrates cutting-edge research with impactful projects to drive meaningful advancements across various domains:
Artificial Intelligence & Machine Learning
Service
Description
Multimodal Fusion of Text, Image, Audio, Video, Time-Series, Graph, 3D LIDAR, etc.
Integrate heterogeneous data sources into joint representations to improve model understanding, classification, or prediction across multiple sensor or input types.
Anomaly Detection in Structured and Unstructured Data
Detecting unusual patterns or outliers using statistical thresholds, autoencoders, or contrastive learning to flag unexpected or critical events in data.
Resource Monitoring and Logging for ML Workloads
Monitor and log performance metrics, GPU/CPU usage, and system events to support debugging, resource planning, and workload optimization.
Deep Fusion of Multimodal Learning Pipelines
Deep fusion of correlated multimodal data in high dimensional embedded space for joint learning and optimization of multiple models for a holistic understanding of the data and more robust and trustworthy output.
Large Language Model & Natural Language Processing
Service
Description
Fine-Tuning of Large Language Models (LLMs) for Domain-Specific Tasks
Adapt pre-trained LLMs to specialized domains via SFT, PEFT (e.g. LoRA), and RL fine-tuning (e.g. DPO, PPO), leveraging customized datasets and strategies.
Text Classification and Topic Modeling
Automatically categorize documents, messages, or transcripts into predefined classes or latent topics using supervised or unsupervised learning (e.g., transformer-based models or LDA).
Named Entity Recognition (NER) and Relation Extraction
Identify and extract entities (e.g., people, locations, organizations) and the semantic relationships between them to enable knowledge graph construction or downstream analytics.
Sentiment Analysis
Determine emotional tone (positive, negative, neutrality) in textual content using rule-based or deep learning models, often applied to public opinion or communication analysis.
Text Summarization (Extractive and Abstractive)
Generate concise summaries of long documents by selecting key sentences (extractive) or generating novel sentences that capture the gist (abstractive), enabling faster information consumption using LLMs.
Prompt Engineering and Optimization for LLM Tasks
Design and test prompts that elicit high-quality, targeted responses from LLMs, including prompt chaining, few-shot examples, and instruction tuning.
Embedding Generation and Similarity Search
Generate vector embeddings from raw inputs and perform similarity comparisons using cosine distance, Euclidean norms, or nearest-neighbor search for retrieval or ranking.
Document Processing / Embedding for LLM Digestion
Extract content from DOCX, PDF, XLSX, raw text; embed for efficient retrieval with re-ranking or vector databases.
Structured Knowledge Extraction from Free Text
Convert unstructured documents into structured formats like tables or triples by extracting entities, attributes, and logical facts using LLMs or rule-based systems.
Retrieval-Augmented Generation (RAG) Pipelines
Create vector databases and combine document retrieval with generative models for grounded LLM responses. Includes management console development.
Computer Vision
Service
Description
Object Detection in Image (Single or Multiple Class)
Training deep learning models to detect objects of interest in image datasets.
Object Detection and Tracking in Video
Detect objects of interest in video frames and maintain their identities across time using tracking algorithms like SORT, Deep SORT, or OC-SORT for robust spatiotemporal analysis.
Image Classification with Confidence Calibration
Classify images into categories while controlling or adjusting the confidence of predictions for high-stakes or decision-critical environments.
Image Segmentation (Binary, Multi-Class)
Assign a class label to each pixel (semantic or instance-wise) in an image, supporting precise object delineation for tasks like medical image analysis or scene understanding.
Video Frame Sampling and Keyframe Analysis
Identify representative frames in a video sequence to reduce computation and focus on moments of highest informational value, often for summarization or event detection.
Visual Explanation and Saliency Map Generation
Visualize which regions of an image contributed most to a model’s prediction using heatmaps or saliency overlays, helping to validate model behavior and build trust.
Integration of Satellite, Drone, or 3D Imagery for Analysis
Process geospatial imagery from varied sources, including overhead or 3D modalities, for environmental monitoring, structure detection, or change detection tasks.
Image Captioning and Visual-Language Model Fusion
Generate descriptive text from images or combine vision and text inputs using models with cross-attention to enable visual question answering or multimodal reasoning.
Modelling
Service
Description
Supervised and Unsupervised Learning Model Development
Design, train, and deploy ML models for both labeled (supervised) and unlabeled (unsupervised) datasets, including but not limited to classification, regression, clustering, object detection, forecasting and segmentation tasks.
Time-Series Forecasting and Temporal Modeling
Develop models that learn from temporal data patterns to predict future outcomes, useful for behavioral modeling, environmental trends, and system monitoring.
Spatio-temporal Data Modeling Using Graph and Transformer Architectures
Combine spatial and temporal relationships into unified models to capture complex dynamics in real-world systems like mobility, population movement, or environmental change.
Custom Loss Function Design and Training Objectives
Define task-specific loss functions that combine multiple training goals, enabling nuanced optimization for complex or imbalanced learning scenarios.
Model Explainability and Interpretability Tools
Use SHAP, or integrated gradients to explain predictions, build trust, and support debugging or stakeholder review.
Spatio-temporal Event Simulation (e.g., Hurricanes, Disasters)
Build simulation environments that replicate real-world events such as hurricanes, wildfires, or floods using historical geospatial data, environmental variables, and physical process models.
Monte Carlo Simulation for Risk and Uncertainty Modeling
Perform probabilistic simulations using Monte Carlo methods to model system behavior under uncertainty, estimate failure probabilities, and support decision-making in dynamic environments.
Statistical Modeling and Threshold Detection
Apply statistical techniques to fit models to observed data, determine thresholds (e.g., anomaly boundaries, danger zones), and validate significance using hypothesis testing and confidence intervals.
Data Engineering & Processing
Service
Description
Data Cleaning, Normalization, and Deduplication
Remove inconsistencies, handle missing values, standardize formats, and eliminate duplicate entries across datasets to ensure data quality before analysis or modeling.
Spatio-temporal Alignment and Data Fusion
Align datasets based on spatial and temporal tolerances to merge complementary signals, supporting unified analysis across location-aware or time-varying data sources.
Custom Parsers and Extract-Transform-Load (ETL) Pipelines
Build modular scripts or workflows to extract data from complex formats, transform it to usable schemas, and load it efficiently into analysis or model training pipelines.
Metadata Generation and Tagging for Large Datasets
Automatically enrich raw data with auxiliary metadata — such as timestamps, subject IDs, semantic tags, or detection contexts — to support better indexing and retrieval.
Data Ingestion from APIs, Databases, or Sensors
Design and maintain scalable ingestion mechanisms that connect to APIs, file systems, or live data streams or scrapes the internet to continuously gather and archive large volumes of structured/unstructured data.
Incremental and Resumable Data Processing Pipelines
Create data pipelines that maintain state and resume automatically after interruption or failure, enabling long-running or fragile operations to complete reliably.
Schema Design for Structured and Semi-Structured Data
Design and enforce consistent data schemas — including table schemas, JSON layouts, or nested data formats — to ensure interoperability and reliability across pipelines and systems.
Data Annotation and Labeling Workflows
Design and implement scalable annotation pipelines for diverse data types including text (e.g., NER, sentiment), images (e.g., classification, segmentation), videos (e.g., object tracking, activity labeling), and graphs (e.g., node/edge labeling). Utilize platforms like Roboflow and custom interfaces to support multi-modal annotation with active learning loops, inter-annotator agreement checks, and quality assurance workflows.
Synthetic Data Generation and Simulation
Generate synthetic datasets using 3D model-based simulations, procedural content generation, or GANs to augment limited or sensitive datasets. Techniques include synthetic video/image rendering in simulated environments (e.g., Unity), time-series and tabular simulation for behavior modeling, and scenario-based sensor data generation for applications in XR, robotics, or autonomous systems.
Human-in-the-Loop Data/ML Workflows
Integrate interactive labeling, feedback loops, and quality control mechanisms into the ML pipeline to improve performance and trust.
Geographic & Temporal Analysis
Service
Description
Spatio-temporal Clustering and Hotspot Detection
Identify regions and time intervals where activity patterns significantly differ from baseline using clustering or statistical methods like DBSCAN, ST-DBSCAN, KDE, etc.
Graph-Based Modeling of Mobility and Proximity Networks
Represent locations and their interactions (e.g., co-location, travel) as graph structures to model relationships and flows over time for spatially-aware learning.
Geographic Visualization with Leaflet, Folium, etc.
Build interactive maps with layered data visualization using web-based libraries to enable exploration of geographic features, patterns, or metrics.
Custom Geospatial Index Generation
Derive composite indices from raw geographic and temporal features — such as accessibility, disruption, or hazard exposure — for ranking or prioritization tasks.
Distance-Based and Topology-Aware Joins for Geographic Datasets
Join or relate geospatial datasets using proximity (e.g., radius or buffer) or structural relationships (e.g., adjacency or containment) rather than strict key matches.
XR/VR/AR
Service
Description
Processing of Behavioral Telemetry from XR/VR Environments
Ingest, clean, and structure raw telemetry logs from virtual environments to create time-aligned sequences for behavior analysis or machine learning input.
3D Motion Sequence Alignment and Similarity Scoring
Align and compare 3D movement trajectories to quantify similarity, identify deviations, or grade performance using temporal alignment and spatial metrics.
Generation of Synthetic Training Data from XR Simulations
Create synthetic sequences through procedural generation or controlled simulation to supplement scarce or imbalanced training data for behavior modeling.
Cybersecurity
Service
Description
Model and Pipeline Vulnerability Assessment
Perform adversarial testing on models, APIs, and full ML pipelines to identify vulnerabilities such as prompt injection, adversarial inputs, data poisoning, and model inversion risks.
Confidential Computing and Encrypted Cloud Execution
Deploy secure computing environments on cloud platforms (e.g., Intel secure enclave, GCP Confidential VMs) to process sensitive data using hardware-backed encryption and secure enclaves.
Secure Authentication and Role-Based Access Control (RBAC)
Implement login systems, API key protection, and RBAC schemes to restrict access based on user roles and maintain secure system operation.
Federated and Privacy-Preserving Machine Learning
Train models collaboratively across decentralized datasets without sharing raw data, preserving privacy through secure aggregation or differential privacy techniques.
Web-Based System
Service
Description
Interactive Dashboard Development Using React
Design and implement web-based dashboards that enable users to explore and interact with complex datasets through dynamic, responsive interfaces.
Mobile Application Design & Development
Create mobile web and native iOS apps with interactive UIs for seamless use across devices.
Integration of LLMs and Chat Agents into User-Facing Apps
Embed large language models into web interfaces to support natural language querying, interactive guidance, or contextual explanations for end-users.
RESTful API and WebSocket Service Development
Design and deploy scalable backend services that expose data and models via REST or real-time WebSocket protocols to support interactive or event-driven applications.
LLM AI Application Design & Development
Develop LLM applications for dialog, audio interface (ASR, TTS), and vector database support.
Scalable Containerized Deployments Using Docker and Kubernetes
Package applications into isolated containers and manage their orchestration and scaling using Docker and Kubernetes for robust, production-ready deployment.
Nginx Configuration for Web Services
Design and maintain Nginx configurations for co-located web servers supporting multiple services.
Software & System Development
Service
Description
Windows Kernel Driver Development
Develop Windows Filtering Platform (WFP) drivers for network monitoring and redirection at multiple WFP layers; handle kernel-user interactions; perform kernel debugging.
Windows Application Development
Design and build Windows applications in C/C++, Go, Python, and other languages.
Asynchronous System Design
Architect decoupled, modular systems using asynchronous design patterns; manage synchronization, state machines, IPC for efficiency and scalability.
Inter-System Network Communication Design
Define application-level protocols for cross-platform messaging; implement using modern web protocols (WebSockets, HTTP/S).
LLM AI Application Design & Development
Develop LLM applications for dialog, audio interface (ASR, TTS), and vector database support.
AI Agent Design & Development
Design custom AI agents from scratch or with frameworks (e.g., MCP) for specialized tasks.
SLM Optimization for Private Deployment
Quantize and optimize small language models for private deployment while maintaining high response accuracy.
Data Visualization
Service
Description
Custom Data Portals for Exploration and Analytics
Implement visualization widgets (e.g., time-based sliders, choropleth maps, bar/line charts) to help users explore patterns over space and time.
Heatmaps, Time Sliders, and Filterable Charts
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Evaluation, Testing, and Optimization
Service
Description
Quantitative Evaluation with Custom Metrics
Design and compute task-specific performance metrics to quantitatively assess model effectiveness beyond standard benchmarks, enabling domain-specific quality control.
Benchmarking Across Multiple Model Variants and Datasets
Compare and document performance across architectures, data splits, and configurations to identify optimal models under varied conditions.
Statistical Testing and Ablation Studies
Perform controlled experiments by isolating model components or data features to determine their contribution, using statistical tests to ensure significance.
Model Robustness and Sensitivity Analysis
Test how model outputs respond to perturbations, data noise, or distribution shifts to ensure stability and generalization across deployment scenarios.
Pipeline-Level Performance Profiling and Bottleneck Detection
Analyze runtime performance and memory use across full data pipelines to identify inefficiencies and optimize throughput, especially in real-time or large-scale deployments.
Unit and Integration Testing for Model Pipelines
Design tests that validate model components and their interactions within full data or API pipelines.
Knowledge Representation and Reasoning
Service
Description
Knowledge Graph Generation and Updating
Automatically convert raw data or extracted facts into graph-based representations with entities and relationships, supporting semantic querying and inference.
Rule-Based and LLM-Based Reasoning Over Structured Knowledge
Implement symbolic rules (e.g., logical or probabilistic) and/or LLM-driven inferences over structured inputs to derive new insights or trigger downstream actions.
Data-to-Graph and Graph-to-Insight Transformations
Transform structured or unstructured data into graphs, then reason over them using algorithms or language models to produce insights, summaries, or recommendations.
Databases and Data Management
Service
Description
Relational and NoSQL Database Design
Design and implement relational (e.g., PostgreSQL) and NoSQL (e.g., MongoDB, Neo4j) databases to store structured, semi-structured, or hierarchical data.
Query Interfaces and API Integration for Data Access
Build standardized query layers and RESTful APIs that provide filtered, aggregated, or paginated access to backend databases for external services or interfaces.
Automated Backup, Migration, and Syncing
Set up automated procedures to back up data and sync multiple database instances to prevent data loss and ensure consistency.
Databases for Retrieval-Augmented Generation (RAG)
Integrate vector databases like Chroma or Pinecone with FAISS to support LLM-powered search and context retrieval for AI applications.
Computing Infrastructure
Service
Description
High-Performance AI Model Training at Scale
dSAIC’s infrastructure includes a total of 26 high-end GPUs with 48 GB VRAM each (from Nvidia A40 and L40S series) and additional GPUs from A30 and Titan V series. This enables the center to support large-scale, memory-intensive AI model training, including deep learning models with massive parameter sizes, fine-tuning of foundation models, and simulation-heavy workloads.
Robust Multi-Node Parallel Processing and Real-Time Inferencing
Equipped with multi-CPU configurations (Intel Xeon and AMD Threadripper) and substantial system memory (up to 256 GB per server), the infrastructure is optimized for parallel data processing, real-time inferencing, and multi-threaded workload management across diverse AI pipelines and use cases.
Scalable, High-Capacity Data Storage and Management
With five enterprise-grade storage servers totaling over 200 TB of raw storage, dSAIC is equipped to manage large datasets, including training corpora, model checkpoints, and inference outputs, supporting efficient data ingestion, versioning, and long-term archiving critical to AI development lifecycles.
Funding Agencies
- National Service Foundation (NSF)
- U.S. Department of Education
- Department of Energy
- National Institutes of Health (NIH)
- Air Force STTR Phases I and II with companies
- Florida Office of Insurance Regulations
- Kauffman Foundation
- Missouri Department of Transportation/ Federal Highway Administration (FHWA)
- Kansas City Health Department
- Microsoft
Computing and Storage Infrastructure
dSAIC is equipped with a state-of-the-art computational environment to support the development, training, and deployment of advanced AI models. The resources span multiple high-performance systems, each optimized for handling large-scale data processing and AI workflows. dSAIC’s infrastructure is equipped with eight servers and five storage servers:
- GPU server 1: This system is configured with 256 GB of system memory and powered by 4 Nvidia A40 GPUs, each with 48 GB of VRAM, offering exceptional performance for memory-intensive AI tasks such as model fine-tuning and large-scale simulations. It is further supported by 2 Intel Xeon Gold 6326 CPUs, ensuring efficient multi-threaded data handling and preprocessing.
- GPU server 2: With 128 GB of system memory, this system hosts 4 Nvidia A30 GPUs, each equipped with 24 GB of VRAM, making it highly efficient for medium-scale AI training tasks and inferencing. The system also utilizes 2 Intel Xeon Gold 6442Y CPUs, providing robust computational power for parallel data pipelines and real-time workload management.
- GPU server 3: With 128 GB of system memory, this system hosts 4 Nvidia A30 GPUs, each equipped with 24 GB of VRAM, making it highly efficient for medium-scale AI training tasks and inferencing. The system also utilizes 2 Intel Xeon Gold 6548Y CPUs, providing robust computational power for parallel data pipelines and real-time workload management.
- GPU server 4: This system is designed for smaller-scale but highly parallelizable tasks, featuring 64 GB of system memory and 2 Nvidia Titan V GPUs, each with 12 GB of VRAM. It is powered by an AMD Ryzen Threadripper Pro 7955WX CPU, a high-performance processor optimized for AI workloads and general computational tasks.
- GPU server 5: As the flagship system at dSAIC, this server boasts 256 GB of system memory and 4 Nvidia L40S GPUs, each with 48 GB of VRAM, providing unparalleled capacity for large-scale model training, particularly for deep learning models with extensive parameter space. The system is supported by 2 Intel Xeon Platinum 8562Y+ CPUs, delivering high-speed computation and superior data throughput, ideal for AI projects requiring real-time scalability and intensive parallel processing.
- GPU server 6: This system, boasts 256 GB of system memory and 4 Nvidia L40S GPUs, each with 48 GB of VRAM, providing capacity for large-scale model training, particularly for deep learning models with extensive parameter space. The system is supported by 2 Intel Xeon Platinum 8562Y+ CPUs, delivering high-speed computation and superior data throughput, ideal for AI projects requiring real-time scalability and intensive parallel processing.
- Storage servers: Two PowerEdge R750 Storage Servers, each with 10 12TB 7.2K RPM SAS 12Gbps 512e 3.5in Hard Drives. Two PowerEdge R760 Storage Servers, each with 12 12TB Hard Drive SAS ISE 12Gbps 7.2K 512e 3.5in Hot-Plug, AG Drive
dSAIC is set to drive key impacts in Workforce Development and Economic Growth:
- Education and Training: Offering specialized courses and certifications.
- Internships and Apprenticeships: Partnering with industry and government for hands-on experience.
- Bootcamps and Upskilling: Focused training in AI, Machine Learning, and cybersecurity.
- Industry Collaborations: Aligning workforce preparation with market needs.
- Economic Growth Initiatives: Driving job creation and investment in AI-driven innovation.
